Maternal Abs by country/arm/exposure
EED biomarkers by country/arm/exposure
n neonatal_shedding dose1_shedding dose2_shedding either_shedding
IND 307 306 305 306 304
MLW 119 102 101 83 72
UK 60 58 60 56 56
seroconv any_response
IND 305 302
MLW 103 61
UK 51 48
proportions positive n percent
1 IND 136 306 44.4
2 MLW 6 102 5.9
3 UK 1 58 1.7
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.4235
proportions positive n percent
1 IND 82 305 26.9
2 MLW 56 101 55.4
3 UK 55 60 91.7
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.0000
proportions positive n percent
1 IND 91 306 29.7
2 MLW 33 83 39.8
3 UK 35 56 62.5
p_values p
1 IND vs MLW 0.0860
2 IND vs UK 0.0000
3 MLW vs UK 0.0148
proportions positive n percent
1 IND 151 304 49.7
2 MLW 50 72 69.4
3 UK 54 56 96.4
p_values p
1 IND vs MLW 0.0025
2 IND vs UK 0.0000
3 MLW vs UK 0.0001
proportions positive n percent
1 IND 22 304 7.2
2 MLW 15 72 20.8
3 UK 32 56 57.1
p_values p
1 IND vs MLW 0.0015
2 IND vs UK 0.0000
3 MLW vs UK 0.0001
From week of life 6 onwards, +2 weeks in the UK owing to later oral rotavirus vaccine schedule.
proportions positive n percent
1 IND 2 307 0.7
2 MLW 1 96 1.0
3 UK 2 58 3.4
p_values p
1 IND vs MLW 0.5590
2 IND vs UK 0.3610
3 MLW vs UK 0.5590
proportions positive n percent
1 IND 136 306 44.4
2 MLW 6 102 5.9
3 UK 1 58 1.7
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.4235
proportions positive n percent
1 IND 0 305 0.0
2 MLW 3 91 3.3
3 UK 4 57 7.0
p_values p
1 IND vs MLW 0.0177
2 IND vs UK 0.0017
3 MLW vs UK 0.4296
proportions positive n percent
1 IND 2 305 0.7
2 MLW 5 88 5.7
3 UK 10 51 19.6
p_values p
1 IND vs MLW 0.0110
2 IND vs UK 0.0000
3 MLW vs UK 0.0203
proportions positive n percent
1 IND 43 307 14.0
2 MLW 36 80 45.0
3 UK 49 59 83.1
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.0000
proportions positive n percent
1 IND 8 304 2.6
2 MLW 9 79 11.4
3 UK 24 54 44.4
p_values p
1 IND vs MLW 0.0026
2 IND vs UK 0.0000
3 MLW vs UK 0.0000
proportions positive n percent
1 IND 37 306 12.1
2 MLW 18 84 21.4
3 UK 29 55 52.7
p_values p
1 IND vs MLW 0.0345
2 IND vs UK 0.0000
3 MLW vs UK 0.0003
IND MLW UK
301 53 50
comp p fdr
1 IND-MLW 4.995433e-03 7.493149e-03
2 UK-MLW 2.664895e-01 2.664895e-01
3 IND-UK 4.574514e-06 1.372354e-05
n neonatal_shedding dose1_shedding dose2_shedding either_shedding
IND (IPV) 100 99 99 99 98
IND (OPV) 207 207 206 207 206
seroconv any_response
IND (IPV) 99 97
IND (OPV) 206 205
proportions positive n percent
1 IND (IPV) 37 99 37.4
2 IND (OPV) 99 207 47.8
p_values p
1 India (IPV) vs India (OPV) 0.1097
proportions positive n percent
1 IND (IPV) 43 99 43.4
2 IND (OPV) 39 206 18.9
p_values p
1 India (IPV) vs India (OPV) 0.0000
proportions positive n percent
1 IND (IPV) 22 99 22.2
2 IND (OPV) 69 207 33.3
p_values p
1 India (IPV) vs India (OPV) 0.0609
proportions positive n percent
1 IND (IPV) 54 98 55.1
2 IND (OPV) 97 206 47.1
p_values p
1 India (IPV) vs India (OPV) 0.2201
proportions positive n percent
1 IND (IPV) 11 98 11.2
2 IND (OPV) 11 206 5.3
p_values p
1 India (IPV) vs India (OPV) 0.0947
proportions positive n percent
1 IND (IPV) 1 100 1.0
2 IND (OPV) 1 207 0.5
p_values p
1 India (IPV) vs India (OPV) 0.5461
proportions positive n percent
1 IND (IPV) 37 99 37.4
2 IND (OPV) 99 207 47.8
p_values p
1 India (IPV) vs India (OPV) 0.1097
Not applicable - no shedding observed in IPV arm or OPV arm in week-4 samples.
proportions positive n percent
1 IND (IPV) 0 100 0
2 IND (OPV) 2 205 1
p_values p
1 India (IPV) vs India (OPV) 1.0000
proportions positive n percent
1 IND (IPV) 23 100 23.0
2 IND (OPV) 20 207 9.7
p_values p
1 India (IPV) vs India (OPV) 0.0026
proportions positive n percent
1 IND (IPV) 6 99 6.1
2 IND (OPV) 2 205 1.0
p_values p
1 India (IPV) vs India (OPV) 0.0162
proportions positive n percent
1 IND (IPV) 6 99 6.1
2 IND (OPV) 31 207 15.0
p_values p
1 India (IPV) vs India (OPV) 0.0251
IND (IPV) IND (OPV)
96 205
comp p
1 OPV-IPV 0.4338797
proportions positive n percent
1 IND 85 305 27.9
2 MLW 24 103 23.3
3 UK 27 51 52.9
p_values p
1 IND vs MLW 0.4398
2 IND vs UK 0.0009
3 MLW vs UK 0.0009
proportions positive n percent
1 IND 99 305 32.5
2 MLW 5 103 4.9
3 UK 2 56 3.6
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 1.0000
proportions positive n percent
1 IND 154 305 50.5
2 MLW 26 103 25.2
3 UK 30 54 55.6
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.5556
3 MLW vs UK 0.0003
gmts n mean lwr.ci upr.ci
1 IND 305 7.53 6.09 9.31
2 MLW 103 3.25 2.66 3.99
3 UK 56 2.65 1.96 3.59
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0001
3 MLW vs UK 0.7334
gmts n mean lwr.ci upr.ci
1 IND 305 19.80 15.76 24.88
2 MLW 103 8.85 6.28 12.47
3 UK 54 27.47 16.89 44.68
p_values p
1 IND vs MLW 0.0009
2 IND vs UK 0.4886
3 MLW vs UK 0.0016
0 1
0 138 31
1 67 68
IND MLW UK
0 138 80 52
1 166 10 2
gmts n mean lwr.ci upr.ci
1 IND 85 92.94 73.02 118.31
2 MLW 24 122.38 79.99 187.24
3 UK 27 105.11 71.13 155.33
p_values p
1 IND vs MLW 0.5115
2 IND vs UK 0.8627
3 MLW vs UK 0.8694
proportions positive n percent
1 IND (IPV) 25 99 25.3
2 IND (OPV) 60 206 29.1
p_values p
1 India (IPV) vs India (OPV) 0.4990
proportions positive n percent
1 IND (IPV) 27 99 27.3
2 IND (OPV) 72 206 35.0
p_values p
1 India (IPV) vs India (OPV) 0.1937
proportions positive n percent
1 IND (IPV) 47 99 47.5
2 IND (OPV) 107 206 51.9
p_values p
1 India (IPV) vs India (OPV) 0.5410
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 6.02 4.14 8.74
2 IND (OPV) 206 8.38 6.47 10.87
p_values p
1 IND (IPV) vs IND (OPV) 0.1503
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 16.88 11.48 24.82
2 IND (OPV) 206 21.37 16.08 28.41
p_values p
1 IND (IPV) vs IND (OPV) 0.3423
IND MLW UK
301 53 47
-- -+ +- ++
IND 53 84 99 65
MLW 16 33 2 2
UK 2 44 0 1
$`s1I$shedding_group`
diff lwr upr p adj
-+--- 0.2517953 -0.07304050 0.5766310 1.892829e-01
+---- 0.9803076 0.66513578 1.2954794 1.041944e-12
++--- 1.3756276 1.03291698 1.7183382 9.058310e-13
+---+ 0.7285123 0.45381817 1.0032065 2.530073e-10
++--+ 1.1238323 0.81793338 1.4297312 9.047207e-13
++-+- 0.3953200 0.09970339 0.6909365 3.510221e-03
$`s1M$shedding_group`
diff lwr upr p adj
-+--- 0.4989606 0.1278144 0.8701068 0.009495288
[1] 138
Pearson's product-moment correlation
data: log(t$MB1_IgA) and log(t$MB1_IgG)
t = 8.254, df = 136, p-value = 1.185e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4544851 0.6792457
sample estimates:
cor
0.5777145
Pearson's product-moment correlation
data: log(t$MB1_IgA) and log(t$BM1_IgA)
t = 2.7965, df = 135, p-value = 0.005921
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.06899184 0.38654509
sample estimates:
cor
0.2339997
gmts n mean lwr.ci upr.ci
1 IND 305 134.02 119.58 150.21
2 MLW 103 340.29 256.31 451.77
3 UK 49 186.05 123.33 280.67
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.1649
3 MLW vs UK 0.0089
gmts n mean lwr.ci upr.ci
1 IND 301 25.28 21.98 29.08
2 MLW 78 96.98 83.66 112.42
3 UK 30 99.64 68.19 145.58
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.9932
gmts n mean lwr.ci upr.ci
1 IND 301 0.62 0.59 0.66
2 MLW 78 0.86 0.76 0.98
3 UK 26 0.96 0.78 1.18
Comparison Z P.unadj P.adj
1 IND - MLW -5.994695 2.038676e-09 6.116028e-09
2 IND - UK -5.157198 2.506725e-07 3.760087e-07
3 MLW - UK -1.291804 1.964250e-01 1.964250e-01
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 151.37 123.00 186.29
2 IND (OPV) 206 126.41 110.26 144.92
p_values p
1 IND (IPV) vs IND (OPV) 0.1456
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 25.77 20.00 33.20
2 IND (OPV) 202 25.05 21.15 29.66
p_values p
1 IND (IPV) vs IND (OPV) 0.8516
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 11150.25 9184.84 13536.24
2 IND (OPV) 206 9538.78 8318.17 10938.50
p_values p
1 IND (IPV) vs IND (OPV) 0.1978
gmts n mean lwr.ci upr.ci
1 IND (IPV) 99 15481.74 12986.50 18456.41
2 IND (OPV) 206 13189.67 11435.85 15212.45
p_values p
1 IND (IPV) vs IND (OPV) 0.1869
gmts n mean lwr.ci upr.ci
1 IND (neo-) 138 131.05 113.21 151.70
2 IND (neo+) 166 137.90 114.93 165.47
p_values p
1 IND (neo+) vs IND (neo-) 0.6633
gmts n mean lwr.ci upr.ci
1 IND (neo-) 137 23.65 19.69 28.40
2 IND (neo+) 163 27.61 22.20 34.34
p_values p
1 IND (neo+) vs IND (neo-) 0.2795
gmts n mean lwr.ci upr.ci
1 IND (neo-) 138 9642.07 8304.85 11194.61
2 IND (neo+) 166 10475.81 8834.78 12421.66
p_values p
1 IND (neo+) vs IND (neo-) 0.4684
gmts n mean lwr.ci upr.ci
1 IND (neo-) 138 13497.15 11761.34 15489.13
2 IND (neo+) 166 14278.52 11873.36 17170.88
p_values p
1 IND (neo+) vs IND (neo-) 0.6230
See Module 5 for full univariate and multivariate analyses.
Pearson's product-moment correlation
data: log(s_sub$MB1_IgA) and log(s_sub$CB1_IgG)
t = 7.8633, df = 303, p-value = 6.625e-14
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3138832 0.5008302
sample estimates:
cor
0.4116784
Pearson's product-moment correlation
data: log(s_sub$MB1_IgA) and log(s_sub$BM1_IgA)
t = 5.3282, df = 299, p-value = 1.955e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1876722 0.3943989
sample estimates:
cor
0.2944767
Number with complete antibody data = 298
Number with complete antibody data = 161
Number with complete antibody data = 137
Pearson's product-moment correlation
data: log(s_sub$MB1_IgA) and log(s_sub$BM1_IgA)
t = 3.6458, df = 76, p-value = 0.0004855
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1786308 0.5602533
sample estimates:
cor
0.3858247
Number with complete antibody data = 64
Number with complete antibody data = 42 (breastmilk IgA data excluded as this reduced the number of complete cases from 40 to 22).
Pearson's product-moment correlation
data: log(s_sub$MB1_IgA) and log(s_sub$BM1_IgA)
t = 1.4668, df = 24, p-value = 0.1554
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.1130915 0.6067642
sample estimates:
cor
0.2868252
India_exposed
MB1 RV-IgA -0.1500
BM1 RV-IgA -0.1191
BB1 RV-IgA 1.0000
MB1 RV-IgG -0.4216
CB1 RV-IgG -0.3843
BB1 RV-IgG 0.2170
India_exposed
MB1 RV-IgA 0.0537
BM1 RV-IgA 0.1298
BB1 RV-IgA 0.0000
MB1 RV-IgG 0.0000
CB1 RV-IgG 0.0000
BB1 RV-IgG 0.0050
India India_neo_pos India_neo_neg Malawi UK
MB1 RV-IgA -0.1298 -0.1816 -0.0930 -0.2884 -0.1168
BM1 RV-IgA -0.1367 -0.1077 -0.1599 -0.2594 0.1924
BB1 RV-IgA 0.7479 0.6888 0.4549 0.4132 0.3303
MB1 RV-IgG -0.2608 -0.3711 -0.1644 NA NA
CB1 RV-IgG -0.2109 -0.3291 -0.1209 NA NA
BB1 RV-IgG 0.1027 0.0136 -0.1134 NA NA
India India_neo_pos India_neo_neg Malawi UK
MB1 RV-IgA 0.0234 0.0192 0.2782 0.0031 0.4446
BM1 RV-IgA 0.0176 0.1710 0.0619 0.0218 0.3175
BB1 RV-IgA 0.0000 0.0000 0.0000 0.0000 0.0179
MB1 RV-IgG 0.0000 0.0000 0.0540 NA NA
CB1 RV-IgG 0.0002 0.0000 0.1579 NA NA
BB1 RV-IgG 0.0734 0.8617 0.1856 NA NA
India India_neo_pos India_neo_neg Malawi UK
1 305 166 138 103 45
2 301 163 137 78 29
3 305 166 138 103 51
4 305 166 138 NA NA
5 305 166 138 NA NA
6 305 166 138 NA NA
Call:
lm(formula = log(BB2_IgA) ~ log(MB1_IgA) + country, data = s)
Residuals:
Min 1Q Median 3Q Max
-3.9242 -1.5926 -0.0985 1.4952 5.3765
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.32175 0.39380 10.975 < 2e-16 ***
log(MB1_IgA) -0.27278 0.07719 -3.534 0.000452 ***
countryMLW -0.55130 0.23086 -2.388 0.017352 *
countryUK 0.43999 0.30847 1.426 0.154453
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.925 on 449 degrees of freedom
(33 observations deleted due to missingness)
Multiple R-squared: 0.06128, Adjusted R-squared: 0.05501
F-statistic: 9.771 on 3 and 449 DF, p-value: 2.956e-06
Call:
lm(formula = log(BB2_IgA) ~ log(MB1_IgA) * factor(country), data = s)
Residuals:
Min 1Q Median 3Q Max
-3.8672 -1.5512 -0.1094 1.4675 5.3505
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.25958 0.54646 7.795 4.57e-14 ***
log(MB1_IgA) -0.26009 0.10927 -2.380 0.0177 *
factor(country)MLW -0.04537 0.96101 -0.047 0.9624
factor(country)UK -0.16984 1.19857 -0.142 0.8874
log(MB1_IgA):factor(country)MLW -0.08881 0.17107 -0.519 0.6039
log(MB1_IgA):factor(country)UK 0.11579 0.22476 0.515 0.6067
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.928 on 447 degrees of freedom
(33 observations deleted due to missingness)
Multiple R-squared: 0.06291, Adjusted R-squared: 0.05243
F-statistic: 6.001 on 5 and 447 DF, p-value: 2.177e-05
Analysis of Variance Table
Model 1: log(BB2_IgA) ~ log(MB1_IgA) + country
Model 2: log(BB2_IgA) ~ log(MB1_IgA) * factor(country)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 449 1663.8
2 447 1660.9 2 2.8825 0.3879 0.6787
Analysis of Variance Table
Model 1: log(BB2_IgA) ~ log(MB1_IgA) + factor(country)
Model 2: log(BB2_IgA) ~ log(MB1_IgA) * factor(country)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 405 1517
2 404 1516 1 1.0014 0.2669 0.6057
Analysis of Variance Table
Model 1: log(BB2_IgA) ~ log(MB1_IgA) + factor(country == "UK")
Model 2: log(BB2_IgA) ~ log(MB1_IgA) * factor(country == "UK")
Res.Df RSS Df Sum of Sq F Pr(>F)
1 450 1684.9
2 449 1681.0 1 3.8696 1.0336 0.3099
Analysis of Variance Table
Model 1: log(BB2_IgA) ~ log(MB1_IgA) + factor(pre_exposed)
Model 2: log(BB2_IgA) ~ log(MB1_IgA) * factor(pre_exposed)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 301 846.95
2 300 843.40 1 3.5517 1.2633 0.2619
No significant evidence that the association between maternal RV-IgA and infant post-vaccination RV-IgA differs by country or between high-income settings (UK) vs LMICs (India/Malawi).
gmts n mean lwr.ci upr.ci
1 IND 301 733.73 676.02 796.37
2 MLW 83 4375.69 3136.03 6105.37
3 UK 48 281.91 155.44 511.28
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0000
3 MLW vs UK 0.0000
gmts n mean lwr.ci upr.ci
1 IND 301 6463.46 5445.64 7671.52
2 MLW 83 2965.95 2454.03 3584.65
3 UK 47 3766.08 2412.79 5878.40
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0399
3 MLW vs UK 0.6234
gmts n mean lwr.ci upr.ci
1 IND 304 722.8 702.8 743.5
2 MLW 87 519 454.1 593.3
$`t$country`
diff lwr upr p adj
MLW-IND -0.3312002 -0.4191556 -0.2432447 0
gmts n mean lwr.ci upr.ci
1 IND 298 820.02 742.48 905.65
2 MLW 63 9221.87 6642.43 12802.97
3 UK 51 507.69 337.76 763.13
p_values p
1 IND vs MLW 0.0000
2 IND vs UK 0.0065
3 MLW vs UK 0.0000
gmts n mean lwr.ci upr.ci
1 IND 298 7327.25 6193.44 8668.61
2 MLW 63 4134.60 3098.09 5517.89
3 UK 51 3082.37 1974.36 4812.20
p_values p
1 IND vs MLW 0.0124
2 IND vs UK 0.0003
3 MLW vs UK 0.5271
gmts n mean lwr.ci upr.ci
1 IND (IPV) 97 759.18 650.11 886.54
2 IND (OPV) 202 719.45 652.68 793.04
p_values p
1 IND (IPV) vs IND (OPV) 0.5486
gmts n mean lwr.ci upr.ci
1 IND (IPV) 97 5291.23 3901.62 7175.75
2 IND (OPV) 202 7040.60 5711.91 8678.36
p_values p
1 IND (IPV) vs IND (OPV) 0.1264
gmts n mean lwr.ci upr.ci
1 IND (IPV) 98 707.87 672.84 744.71
2 IND (OPV) 205 729.80 705.28 755.16
p_values p
1 IND (IPV) vs IND (OPV) 0.3207
gmts n mean lwr.ci upr.ci
1 IND (IPV) 96 794.26 673.12 937.22
2 IND (OPV) 200 829.23 731.31 940.25
p_values p
1 IND (IPV) vs IND (OPV) 0.6920
gmts n mean lwr.ci upr.ci
1 IND (IPV) 96 7072.33 5319.52 9402.70
2 IND (OPV) 200 7597.93 6160.52 9370.73
p_values p
1 IND (IPV) vs IND (OPV) 0.6954
gmts n mean lwr.ci upr.ci
1 IND (neo-) 136 713.51 638.96 796.76
2 IND (neo+) 163 755.02 666.28 855.59
p_values p
1 IND (neo+) vs IND (neo-) 0.5020
gmts n mean lwr.ci upr.ci
1 IND (neo-) 136 6364.62 5042.66 8033.13
2 IND (neo+) 163 6481.43 5003.58 8395.77
p_values p
1 IND (neo+) vs IND (neo-) 0.9177
gmts n mean lwr.ci upr.ci
1 IND (neo-) 138 713.85 686.98 741.77
2 IND (neo+) 165 733.27 703.05 764.79
p_values p
1 IND (neo+) vs IND (neo-) 0.3524
gmts n mean lwr.ci upr.ci
1 IND (neo-) 134 770.88 667.28 890.56
2 IND (neo+) 162 878.17 766.32 1006.35
p_values p
1 IND (neo+) vs IND (neo-) 0.2020
gmts n mean lwr.ci upr.ci
1 IND (neo-) 134 6610.54 5233.69 8349.60
2 IND (neo+) 162 8540.50 6699.34 10887.65
p_values p
1 IND (neo+) vs IND (neo-) 0.1361
See Module 5 for full univariate and multivariate analyses.
India_neo_pos
α1AT (week 6) 0.0581
α1AT (week 10) -0.0204
MPO (week 6) 0.1368
MPO (week 10) 0.0387
α1AG (week 6) 0.0306
India_neo_pos
α1AT (week 6) 0.4617
α1AT (week 10) 0.7965
MPO (week 6) 0.0817
MPO (week 10) 0.6252
α1AG (week 6) 0.6967
India
1 163
2 162
3 163
4 162
5 165
India India_neo_pos India_neo_neg Malawi UK
α1AT (week of life 6) -0.0325 0.0597 -0.0996 -0.0601 -0.0815
α1AT (week of life 10) -0.0624 -0.0298 -0.0102 0.0835 0.2202
MPO (week of life 6) -0.0294 0.0343 -0.1183 -0.0318 -0.0388
MPO (week of life 10) -0.1112 -0.0618 -0.1239 0.0242 -0.2363
α1AG (week of life 6) -0.0818 -0.1074 0.0041 -0.0230 NA
India India_neo_pos India_neo_neg Malawi UK
α1AT (week of life 6) 0.5748 0.4489 0.2485 0.6013 0.6036
α1AT (week of life 10) 0.2836 0.7064 0.9069 0.5521 0.1325
MPO (week of life 6) 0.6125 0.6641 0.1702 0.7824 0.8072
MPO (week of life 10) 0.0556 0.4347 0.1538 0.8637 0.1138
α1AG (week of life 6) 0.1546 0.1699 0.9617 0.8327 NA
India India_neo_pos India_neo_neg Malawi UK
1 300 163 136 78 43
2 297 162 134 53 48
3 300 163 136 78 42
4 297 162 134 53 46
5 304 165 138 87 NA
IND MLW UK
0 138 80 52
1 166 10 2
Pre-exposure defined as detection of week-1 shedding (VP6 Ct <35) or seropositivity pre-vaccination (RV-IgA ≥20 IU/ml)
proportions positive n percent p
1 IND (neo-) 52 138 37.7 1e-04
2 IND (neo+) 29 164 17.7 NA
proportions positive n percent p
1 IND (neo-) 84 137 61.3 2e-04
2 IND (neo+) 65 164 39.6 NA
proportions positive n percent p
1 IND (neo-) 34 138 24.6 0.2508
2 IND (neo+) 51 166 30.7 NA
group mean lwr.ci upr.ci p
1 IND (neo-) 1.9 1.7 2.2 0
2 IND (neo+) 23.7 18.1 31.1 NA
group mean lwr.ci upr.ci p
1 IND (neo-) 5.9 4.6 7.6 0
2 IND (neo+) 55.1 41.8 72.8 NA
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] FSA_0.9.0 ALDEx2_1.18.0
[3] sjstats_0.18.0 ggExtra_0.9
[5] formattable_0.2.0.1 NBZIMM_1.0
[7] inlmisc_0.5.2 decontam_1.6.0
[9] ggtree_2.0.4 wesanderson_0.3.6
[11] phangorn_2.5.5 ape_5.4-1
[13] DECIPHER_2.14.0 RSQLite_2.2.1
[15] Biostrings_2.54.0 XVector_0.26.0
[17] cowplot_1.1.0 scales_1.1.1
[19] RVAideMemoire_0.9-78 DescTools_0.99.38
[21] ggsignif_0.6.0 binom_1.1-1
[23] shiny_1.5.0 randomcoloR_1.1.0.1
[25] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[27] DelayedArray_0.12.3 BiocParallel_1.20.1
[29] Biobase_2.46.0 GenomicRanges_1.38.0
[31] GenomeInfoDb_1.22.1 IRanges_2.20.2
[33] S4Vectors_0.24.4 BiocGenerics_0.32.0
[35] crossval_1.0.3 UpSetR_1.4.0
[37] labdsv_2.0-1 mgcv_1.8-33
[39] nlme_3.1-149 ggpubr_0.4.0
[41] data.table_1.12.8 corrplot_0.84
[43] ZIBR_0.1 vegan_2.5-6
[45] lattice_0.20-41 permute_0.9-5
[47] randomForest_4.6-14 matrixStats_0.57.0
[49] lme4_1.1-23 Matrix_1.2-18
[51] reshape2_1.4.4 pheatmap_1.0.12
[53] DT_0.16 plotly_4.9.3
[55] cluster_2.1.0 tidyr_1.1.2
[57] dplyr_1.0.2 magrittr_1.5
[59] plyr_1.8.6 kableExtra_1.2.1
[61] gridExtra_2.3 RColorBrewer_1.1-2
[63] knitr_1.30 ggplot2_3.3.2
[65] phyloseq_1.30.0
loaded via a namespace (and not attached):
[1] estimability_1.3 coda_0.19-4 bit64_4.0.5
[4] rpart_4.1-15 RCurl_1.98-1.2 generics_0.0.2
[7] bit_4.0.4 webshot_0.5.2 xml2_1.3.2
[10] httpuv_1.5.4 xfun_0.18 hms_0.5.3
[13] evaluate_0.14 promises_1.1.1 readxl_1.3.1
[16] igraph_1.2.6 DBI_1.1.0 geneplotter_1.64.0
[19] htmlwidgets_1.5.3 purrr_0.3.4 ellipsis_0.3.1
[22] backports_1.1.10 V8_3.2.0 insight_0.9.6
[25] annotate_1.64.0 vctrs_0.3.4 sjlabelled_1.1.7
[28] abind_1.4-5 withr_2.3.0 checkmate_2.0.0
[31] rgdal_1.5-18 emmeans_1.5.1 treeio_1.10.0
[34] lazyeval_0.2.2 crayon_1.3.4 flexdashboard_0.5.2
[37] genefilter_1.68.0 labeling_0.3 pkgconfig_2.0.3
[40] nnet_7.3-14 rlang_0.4.8 lifecycle_0.2.0
[43] miniUI_0.1.1.1 modelr_0.1.8 cellranger_1.1.0
[46] raster_3.3-13 carData_3.0-4 Rhdf5lib_1.8.0
[49] boot_1.3-25 base64enc_0.1-3 png_0.1-7
[52] viridisLite_0.3.0 dunn.test_1.3.5 parameters_0.8.6
[55] rootSolve_1.8.2.1 bitops_1.0-6 blob_1.2.1
[58] stringr_1.4.0 jpeg_0.1-8.1 rstatix_0.6.0
[61] memoise_1.1.0 zlibbioc_1.32.0 compiler_3.6.1
[64] ade4_1.7-15 htmlTable_2.1.0 Formula_1.2-4
[67] MASS_7.3-53 tidyselect_1.1.0 stringi_1.5.3
[70] forcats_0.5.0 yaml_2.2.1 locfit_1.5-9.4
[73] latticeExtra_0.6-29 grid_3.6.1 fastmatch_1.1-0
[76] tools_3.6.1 lmom_2.8 rio_0.5.16
[79] rstudioapi_0.11 foreach_1.5.1 foreign_0.8-71
[82] gld_2.6.2 farver_2.0.3 Rtsne_0.15
[85] digest_0.6.25 rvcheck_0.1.8 BiocManager_1.30.10
[88] quadprog_1.5-8 Rcpp_1.0.5 car_3.0-10
[91] broom_0.7.1 performance_0.5.0 later_1.1.0.1
[94] httr_1.4.2 AnnotationDbi_1.48.0 effectsize_0.3.3
[97] colorspace_1.4-1 rvest_0.3.6 XML_3.99-0.3
[100] splines_3.6.1 statmod_1.4.34 tidytree_0.3.3
[103] expm_0.999-5 sp_1.4-4 multtest_2.42.0
[106] Exact_2.1 xtable_1.8-4 jsonlite_1.7.1
[109] nloptr_1.2.2.2 R6_2.4.1 Hmisc_4.4-1
[112] pillar_1.4.6 htmltools_0.5.0 mime_0.9
[115] glue_1.4.2 fastmap_1.0.1 minqa_1.2.4
[118] class_7.3-17 codetools_0.2-17 mvtnorm_1.1-1
[121] tibble_3.0.4 curl_4.3 zip_2.1.1
[124] openxlsx_4.2.2 survival_3.2-7 rmarkdown_2.4
[127] biomformat_1.14.0 munsell_0.5.0 e1071_1.7-4
[130] rhdf5_2.30.1 GenomeInfoDbData_1.2.2 iterators_1.0.13
[133] sjmisc_2.8.5 haven_2.3.1 gtable_0.3.0
[136] bayestestR_0.7.2
.